Schuurmans, Dale

Chain-Of-Thought Prompting Elicits Reasoning in Large Language Models

Wei, J., Wang, X., Schuurmans, D., Bosma, M., Ichter, B., Xia, F., Chi, E., Le, Q. V., & Zhou, D. (2022) · Advances in Neural Information Processing Systems 35

Topic ModelingNatural Language Processing Techniques

Cited by 4,505

Chain-of-Thought Prompting Elicits Reasoning in Large Language Models

Wei, J., Wang, X., Schuurmans, D., Bosma, M., Ichter, B., Xia, F., Chi, E., Le, Q., & Zhou, D. (2022) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 4,263

Self-Consistency Improves Chain of Thought Reasoning in Language Models

Wang, X., Wei, J., Schuurmans, D., Le, Q., Chi, E., Narang, S., Chowdhery, A., & Zhou, D. (2022) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 829

Least-to-Most Prompting Enables Complex Reasoning in Large Language Models

Zhou, D., Schärli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Cui, C., Bousquet, O., Le, Q., et al. (2022) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 380

Learning Interactive Real-World Simulators

Yang, S., Du, Y., Ghasemipour, K., Tompson, J., Kaelbling, L., Schuurmans, D., & Abbeel, P. (2023) · arXiv

Reinforcement Learning in RoboticsMultimodal Machine Learning Applications

Cited by 346

What learning algorithm is in-context learning? Investigations with linear models

Akyürek, E., Schuurmans, D., Andreas, J., Ma, T., & Zhou, D. (2022) · arXiv

Domain Adaptation and Few-Shot LearningNeural Networks and Applications

Cited by 86

Foundation Models for Decision Making: Problems, Methods, and Opportunities

Yang, S., Nachum, O., Du, Y., Wei, J., Abbeel, P., & Schuurmans, D. (2023) · arXiv

Multimodal Machine Learning ApplicationsAI-based Problem Solving and Planning

Cited by 60

SFT Memorizes, RL Generalizes: A Comparative Study of Foundation Model Post-training

Chu, T., Zhai, Y., Yang, J., Tong, S., Xie, S., Schuurmans, D., Le, Q. V., Levine, S., & Ma, Y. (2025) · arXiv

Domain Adaptation and Few-Shot LearningReinforcement Learning in Robotics

Cited by 7

Dichotomy of Control: Separating What You Can Control from What You Cannot

Yang, M., Schuurmans, D., Abbeel, P., & Nachum, O. (2022) · arXiv

Reinforcement Learning in RoboticsDomain Adaptation and Few-Shot Learning

Cited by 6

Evolving Deeper LLM Thinking

Lee, K.-H., Fischer, I., Wu, Y.-H., Marwood, D., Baluja, S., Schuurmans, D., & Chen, X. (2025) · arXiv

Topic ModelingAI-based Problem Solving and Planning

Cited by 2

Improving Large Language Model Planning with Action Sequence Similarity

Zhao, X., Sedghi, H., Bohnet, B., Schuurmans, D., & Nova, A. (2025) · arXiv

AI-based Problem Solving and PlanningTopic Modeling

Cited by 0